Bottom Line:
The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines.The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments.The body segment parameters of six participants (four male and two female) are presented using the proposed method.

Affiliation: Faculty of Life Sciences, University of Manchester , Manchester , United Kingdom.

ABSTRACTInertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

Mentions:
Six participants were scanned using the RPi photogrammetry setup and their point cloud segmented. In order to be able to calculate the inertial properties, the point cloud needs to be converted into a closed surface mesh. To calculate the volume of an arbitrary shape defined by a surface mesh, the mesh needs to be well defined, i.e., it should be two-manifold, contain no holes in the mesh, and have coherent face orientations. The process of converting a point cloud to a well-defined mesh is known as hulling and there are several possible methods available. The simplest is the minimum convex hull where the minimum volume convex shape is derived mathematically from the point cloud (www.qhull.org). This approach has the advantage of being extremely quick and easy to perform and it is very tolerant of point clouds that may contain holes where the reconstruction algorithm has partially failed. However, it will always overestimate the volume unless the shape is convex. There are also a number of concave hulling approaches. Some are mathematically defined such as AlphaShapes (Edelsbrunner & Mücke, 1994) and Ball Pivoting (Bernardini et al., 1999) and require additional parameters defining the maximum level of permitted convexity. Others are proprietary and can require considerable manual intervention such as the built in hole-filling algorithms in Geomagic. This latter group provides the highest quality reconstructions but at the expense of considerable operator time. For this paper, we concentrated on convex hulls under the assumption that the level of concavity in individual body segments was likely to be relatively small. The relative segment mass of all participants are reported in Fig. 3 (the segmented convex hulls are shown in Fig. S1 in Supplemental Information). Figure 3 also displays average values from literature. As the participants were imaged wearing shoes, the foot volume was overestimated significantly. It is possible to adjust the value using a foot-specific scaling factor that accounts for this overestimation, although of course if the subsequent use of the BSP parameters is in experiments with participants wearing shoes then the shoe mass becomes an important part of the segment. For the purpose of this paper, a scaling factor was derived based on a single participant (P5) by comparing the convex hull volume of the foot imaged in socks versus the convex hull volume wearing shoes, and this factor (of 0.51) applied to all participants’ inertial values of the feet. The moments of inertia are shown in Fig. 4 together with average values from literature. Geometric methods also allow us to calculate the products of inertia which are otherwise simply assumed to be zero. The average products of inertia are depicted in Fig. 5 (absolute values shown only, signed values reported in Supplemental Information (Tables S2–S4). Some segments, e.g., the thigh or trunk, have products of inertia that are of a similar order of magnitude as their moments of inertia, which is indicative of a noticeable difference between the inertial principal axes and the anatomical principal axes. However, the majority of the products of inertia are significantly smaller than the moments of inertia (of the same segment) by one to two orders of magnitude. Figure 6 contains the relative centre of mass in the longitudinal segment direction, i.e., along the z-axis with the exception of the foot whose longitudinal axis corresponds to the x-axis (see Fig. 2). Figure 7 shows the shift of CoM from the longitudinal axis in the transverse plane (x–y plane). The CoM values in literature assume a zero shift from the principal anatomical (longitudinal) axis. The shift values we found with our geometric method are generally unequal to zero, but they have be to viewed with caution as the placement of the reference anatomical axis itself has uncertainties associated with it. The numerical values presented in Figs. 3–7 and the segment lengths are reported in Supplemental Information (Tables S2–S13).

Mentions:
Six participants were scanned using the RPi photogrammetry setup and their point cloud segmented. In order to be able to calculate the inertial properties, the point cloud needs to be converted into a closed surface mesh. To calculate the volume of an arbitrary shape defined by a surface mesh, the mesh needs to be well defined, i.e., it should be two-manifold, contain no holes in the mesh, and have coherent face orientations. The process of converting a point cloud to a well-defined mesh is known as hulling and there are several possible methods available. The simplest is the minimum convex hull where the minimum volume convex shape is derived mathematically from the point cloud (www.qhull.org). This approach has the advantage of being extremely quick and easy to perform and it is very tolerant of point clouds that may contain holes where the reconstruction algorithm has partially failed. However, it will always overestimate the volume unless the shape is convex. There are also a number of concave hulling approaches. Some are mathematically defined such as AlphaShapes (Edelsbrunner & Mücke, 1994) and Ball Pivoting (Bernardini et al., 1999) and require additional parameters defining the maximum level of permitted convexity. Others are proprietary and can require considerable manual intervention such as the built in hole-filling algorithms in Geomagic. This latter group provides the highest quality reconstructions but at the expense of considerable operator time. For this paper, we concentrated on convex hulls under the assumption that the level of concavity in individual body segments was likely to be relatively small. The relative segment mass of all participants are reported in Fig. 3 (the segmented convex hulls are shown in Fig. S1 in Supplemental Information). Figure 3 also displays average values from literature. As the participants were imaged wearing shoes, the foot volume was overestimated significantly. It is possible to adjust the value using a foot-specific scaling factor that accounts for this overestimation, although of course if the subsequent use of the BSP parameters is in experiments with participants wearing shoes then the shoe mass becomes an important part of the segment. For the purpose of this paper, a scaling factor was derived based on a single participant (P5) by comparing the convex hull volume of the foot imaged in socks versus the convex hull volume wearing shoes, and this factor (of 0.51) applied to all participants’ inertial values of the feet. The moments of inertia are shown in Fig. 4 together with average values from literature. Geometric methods also allow us to calculate the products of inertia which are otherwise simply assumed to be zero. The average products of inertia are depicted in Fig. 5 (absolute values shown only, signed values reported in Supplemental Information (Tables S2–S4). Some segments, e.g., the thigh or trunk, have products of inertia that are of a similar order of magnitude as their moments of inertia, which is indicative of a noticeable difference between the inertial principal axes and the anatomical principal axes. However, the majority of the products of inertia are significantly smaller than the moments of inertia (of the same segment) by one to two orders of magnitude. Figure 6 contains the relative centre of mass in the longitudinal segment direction, i.e., along the z-axis with the exception of the foot whose longitudinal axis corresponds to the x-axis (see Fig. 2). Figure 7 shows the shift of CoM from the longitudinal axis in the transverse plane (x–y plane). The CoM values in literature assume a zero shift from the principal anatomical (longitudinal) axis. The shift values we found with our geometric method are generally unequal to zero, but they have be to viewed with caution as the placement of the reference anatomical axis itself has uncertainties associated with it. The numerical values presented in Figs. 3–7 and the segment lengths are reported in Supplemental Information (Tables S2–S13).

Bottom Line:
The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines.The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments.The body segment parameters of six participants (four male and two female) are presented using the proposed method.

Affiliation:
Faculty of Life Sciences, University of Manchester , Manchester , United Kingdom.

ABSTRACTInertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.